Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping

Simon Höllerer, Laetitia Papaxanthos, Anja Cathrin Gumpinger, Katrin Fischer, Christian Beisel, Karsten Borgwardt (), Yaakov Benenson () and Markus Jeschek ()
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Simon Höllerer: ETH Zurich
Laetitia Papaxanthos: ETH Zurich
Anja Cathrin Gumpinger: ETH Zurich
Katrin Fischer: ETH Zurich
Christian Beisel: ETH Zurich
Karsten Borgwardt: ETH Zurich
Yaakov Benenson: ETH Zurich
Markus Jeschek: ETH Zurich

Nature Communications, 2020, vol. 11, issue 1, 1-15

Abstract: Abstract Predicting effects of gene regulatory elements (GREs) is a longstanding challenge in biology. Machine learning may address this, but requires large datasets linking GREs to their quantitative function. However, experimental methods to generate such datasets are either application-specific or technically complex and error-prone. Here, we introduce DNA-based phenotypic recording as a widely applicable, practicable approach to generate large-scale sequence-function datasets. We use a site-specific recombinase to directly record a GRE’s effect in DNA, enabling readout of both sequence and quantitative function for extremely large GRE-sets via next-generation sequencing. We record translation kinetics of over 300,000 bacterial ribosome binding sites (RBSs) in >2.7 million sequence-function pairs in a single experiment. Further, we introduce a deep learning approach employing ensembling and uncertainty modelling that predicts RBS function with high accuracy, outperforming state-of-the-art methods. DNA-based phenotypic recording combined with deep learning represents a major advance in our ability to predict function from genetic sequence.

Date: 2020
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DOI: 10.1038/s41467-020-17222-4

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